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In a rapidly growing data environment, a key challenge is to build models directly from the data. The models can be used to make predictions or forecasts of different outcomes.  In order to make prediction a capability that more people can use routinely, the ideal product should allow users to access heterogeneous data environments, understand their models to enable transparent decision making, update their models efficiently with new data, and easily tune their models to exhibit desired characteristics.

Quantum Leap Pattern Based Prediction automatically generates ensembles of predictive models from informative patterns discovered in the data.  Key features include:

1. Ease of Use - Automatic generation of informative models from data
   

2. Transparency - Models are transparent and can incorporate human knowledge
   

3. Interoperability - Models can be mapped as SQL queries
   

4. Scalability - Designed to efficiently leverage Big Data
   

5. Dynamic - Models can be updated with new data
   

6. Flexibility - Models can be easily tuned to meet user requirements
   

Quantum Leap Pattern Based Prediction in Healthcare and Life Sciences applications

1. Drug Discovery and Development
   

2. •Predict biochemical activity of candidate compounds based on the most informative structural subcomponents
   

3. •Discover “needles in haystacks” in Big Data
   

4. •Build integrative models across diverse data sets that can span multiple biological scales (spanning the “omics continuum”)
   

5. Prediction of Health Outcomes
   

6. •Predict health outcomes from the integrated healthcare data environments that include patient information, treatments and health outcomes.
   

7. •Scale to Big Data through the generation of extremely compact and informative models
   

Overview

Pattern Based Discovery

Pattern Based Prediction

Pattern Based Reasoning

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